# coding=utf8
"""
Label propagation in the context of this module refers to a set of
semi-supervised classification algorithms. At a high level, these algorithms
work by forming a fully-connected graph between all points given and solving
for the steady-state distribution of labels at each point.

These algorithms perform very well in practice. The cost of running can be very
expensive, at approximately O(N^3) where N is the number of (labeled and
unlabeled) points. The theory (why they perform so well) is motivated by
intuitions from random walk algorithms and geometric relationships in the data.
For more information see the references below.

Model Features
--------------
Label clamping:
  The algorithm tries to learn distributions of labels over the dataset. In the
  "Hard Clamp" mode, the true ground labels are never allowed to change. They
  are clamped into position. In the "Soft Clamp" mode, they are allowed some
  wiggle room, but some alpha of their original value will always be retained.
  Hard clamp is the same as soft clamping with alpha set to 1.

Kernel:
  A function which projects a vector into some higher dimensional space. This
  implementation supports RBF and KNN kernels. Using the RBF kernel generates
  a dense matrix of size O(N^2). KNN kernel will generate a sparse matrix of
  size O(k*N) which will run much faster. See the documentation for SVMs for
  more info on kernels.

Examples
--------
>>> from sklearn import datasets
>>> from sklearn.semi_supervised import LabelPropagation
>>> label_prop_model = LabelPropagation()
>>> iris = datasets.load_iris()
>>> random_unlabeled_points = np.where(np.random.randint(0, 2,
...        size=len(iris.target)))
>>> labels = np.copy(iris.target)
>>> labels[random_unlabeled_points] = -1
>>> label_prop_model.fit(iris.data, labels)
... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
LabelPropagation(...)

Notes
-----
References:
[1] Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux. In Semi-Supervised
Learning (2006), pp. 193-216

[2] Olivier Delalleau, Yoshua Bengio, Nicolas Le Roux. Efficient
Non-Parametric Function Induction in Semi-Supervised Learning. AISTAT 2005
"""

# Authors: Clay Woolam <clay@woolam.org>
# License: BSD
from abc import ABCMeta, abstractmethod

import numpy as np
from scipy import sparse

from ..base import BaseEstimator, ClassifierMixin
from ..externals import six
from ..metrics.pairwise import rbf_kernel
from ..neighbors.unsupervised import NearestNeighbors
from ..utils.extmath import safe_sparse_dot
from ..utils.graph import graph_laplacian
from ..utils.multiclass import check_classification_targets
from ..utils.validation import check_X_y, check_is_fitted, check_array


# Helper functions

def _not_converged(y_truth, y_prediction, tol=1e-3):
    """basic convergence check"""
    return np.abs(y_truth - y_prediction).sum() > tol


class BaseLabelPropagation(six.with_metaclass(ABCMeta, BaseEstimator,
                                              ClassifierMixin)):
    """Base class for label propagation module.

    Parameters
    ----------
    kernel : {'knn', 'rbf', callable}
        String identifier for kernel function to use or the kernel function
        itself. Only 'rbf' and 'knn' strings are valid inputs. The function
        passed should take two inputs, each of shape [n_samples, n_features],
        and return a [n_samples, n_samples] shaped weight matrix

    gamma : float
        Parameter for rbf kernel

    alpha : float
        Clamping factor

    max_iter : float
        Change maximum number of iterations allowed

    tol : float
        Convergence tolerance: threshold to consider the system at steady
        state

    n_neighbors : integer > 0
        Parameter for knn kernel

    n_jobs : int, optional (default = 1)
        The number of parallel jobs to run.
        If ``-1``, then the number of jobs is set to the number of CPU cores.
    """

    def __init__(self, kernel='rbf', gamma=20, n_neighbors=7,
                 alpha=1, max_iter=30, tol=1e-3, n_jobs=1):

        self.max_iter = max_iter
        self.tol = tol

        # kernel parameters
        self.kernel = kernel
        self.gamma = gamma
        self.n_neighbors = n_neighbors

        # clamping factor
        self.alpha = alpha

        self.n_jobs = n_jobs

    def _get_kernel(self, X, y=None):
        if self.kernel == "rbf":
            if y is None:
                return rbf_kernel(X, X, gamma=self.gamma)
            else:
                return rbf_kernel(X, y, gamma=self.gamma)
        elif self.kernel == "knn":
            if self.nn_fit is None:
                self.nn_fit = NearestNeighbors(self.n_neighbors,
                                               n_jobs=self.n_jobs).fit(X)
            if y is None:
                return self.nn_fit.kneighbors_graph(self.nn_fit._fit_X,
                                                    self.n_neighbors,
                                                    mode='connectivity')
            else:
                return self.nn_fit.kneighbors(y, return_distance=False)
        elif callable(self.kernel):
            if y is None:
                return self.kernel(X, X)
            else:
                return self.kernel(X, y)
        else:
            raise ValueError("%s is not a valid kernel. Only rbf and knn"
                             " or an explicit function "
                             " are supported at this time." % self.kernel)

    @abstractmethod
    def _build_graph(self):
        raise NotImplementedError("Graph construction must be implemented"
                                  " to fit a label propagation model.")

    def predict(self, X):
        """Performs inductive inference across the model.

        Parameters
        ----------
        X : array_like, shape = [n_samples, n_features]

        Returns
        -------
        y : array_like, shape = [n_samples]
            Predictions for input data
        """
        probas = self.predict_proba(X)
        return self.classes_[np.argmax(probas, axis=1)].ravel()

    def predict_proba(self, X):
        """Predict probability for each possible outcome.

        Compute the probability estimates for each single sample in X
        and each possible outcome seen during training (categorical
        distribution).

        Parameters
        ----------
        X : array_like, shape = [n_samples, n_features]

        Returns
        -------
        probabilities : array, shape = [n_samples, n_classes]
            Normalized probability distributions across
            class labels
        """
        check_is_fitted(self, 'X_')

        X_2d = check_array(X, accept_sparse=['csc', 'csr', 'coo', 'dok',
                                             'bsr', 'lil', 'dia'])
        weight_matrices = self._get_kernel(self.X_, X_2d)
        if self.kernel == 'knn':
            probabilities = []
            for weight_matrix in weight_matrices:
                ine = np.sum(self.label_distributions_[weight_matrix], axis=0)
                probabilities.append(ine)
            probabilities = np.array(probabilities)
        else:
            weight_matrices = weight_matrices.T
            probabilities = np.dot(weight_matrices, self.label_distributions_)
        normalizer = np.atleast_2d(np.sum(probabilities, axis=1)).T
        probabilities /= normalizer
        return probabilities

    def fit(self, X, y):
        """Fit a semi-supervised label propagation model based

        All the input data is provided matrix X (labeled and unlabeled)
        and corresponding label matrix y with a dedicated marker value for
        unlabeled samples.

        Parameters
        ----------
        X : array-like, shape = [n_samples, n_features]
            A {n_samples by n_samples} size matrix will be created from this

        y : array_like, shape = [n_samples]
            n_labeled_samples (unlabeled points are marked as -1)
            All unlabeled samples will be transductively assigned labels

        Returns
        -------
        self : returns an instance of self.
        """
        X, y = check_X_y(X, y)
        self.X_ = X
        check_classification_targets(y)

        # actual graph construction (implementations should override this)
        graph_matrix = self._build_graph()

        # label construction
        # construct a categorical distribution for classification only
        classes = np.unique(y)
        classes = (classes[classes != -1])
        self.classes_ = classes

        n_samples, n_classes = len(y), len(classes)

        y = np.asarray(y)
        unlabeled = y == -1
        clamp_weights = np.ones((n_samples, 1))
        clamp_weights[unlabeled, 0] = self.alpha

        # initialize distributions
        self.label_distributions_ = np.zeros((n_samples, n_classes))
        for label in classes:
            self.label_distributions_[y == label, classes == label] = 1

        y_static = np.copy(self.label_distributions_)
        if self.alpha > 0.:
            y_static *= 1 - self.alpha
        y_static[unlabeled] = 0

        l_previous = np.zeros((self.X_.shape[0], n_classes))

        remaining_iter = self.max_iter
        if sparse.isspmatrix(graph_matrix):
            graph_matrix = graph_matrix.tocsr()
        while (_not_converged(self.label_distributions_, l_previous, self.tol)
               and remaining_iter > 1):
            l_previous = self.label_distributions_
            self.label_distributions_ = safe_sparse_dot(
                graph_matrix, self.label_distributions_)
            # clamp
            self.label_distributions_ = np.multiply(
                clamp_weights, self.label_distributions_) + y_static
            remaining_iter -= 1

        normalizer = np.sum(self.label_distributions_, axis=1)[:, np.newaxis]
        self.label_distributions_ /= normalizer
        # set the transduction item
        transduction = self.classes_[np.argmax(self.label_distributions_,
                                               axis=1)]
        self.transduction_ = transduction.ravel()
        self.n_iter_ = self.max_iter - remaining_iter
        return self


class LabelPropagation(BaseLabelPropagation):
    """Label Propagation classifier

    Read more in the :ref:`User Guide <label_propagation>`.

    Parameters
    ----------
    kernel : {'knn', 'rbf', callable}
        String identifier for kernel function to use or the kernel function
        itself. Only 'rbf' and 'knn' strings are valid inputs. The function
        passed should take two inputs, each of shape [n_samples, n_features],
        and return a [n_samples, n_samples] shaped weight matrix.

    gamma : float
        Parameter for rbf kernel

    n_neighbors : integer > 0
        Parameter for knn kernel

    alpha : float
        Clamping factor

    max_iter : float
        Change maximum number of iterations allowed

    tol : float
        Convergence tolerance: threshold to consider the system at steady
        state

    Attributes
    ----------
    X_ : array, shape = [n_samples, n_features]
        Input array.

    classes_ : array, shape = [n_classes]
        The distinct labels used in classifying instances.

    label_distributions_ : array, shape = [n_samples, n_classes]
        Categorical distribution for each item.

    transduction_ : array, shape = [n_samples]
        Label assigned to each item via the transduction.

    n_iter_ : int
        Number of iterations run.

    Examples
    --------
    >>> from sklearn import datasets
    >>> from sklearn.semi_supervised import LabelPropagation
    >>> label_prop_model = LabelPropagation()
    >>> iris = datasets.load_iris()
    >>> random_unlabeled_points = np.where(np.random.randint(0, 2,
    ...    size=len(iris.target)))
    >>> labels = np.copy(iris.target)
    >>> labels[random_unlabeled_points] = -1
    >>> label_prop_model.fit(iris.data, labels)
    ... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
    LabelPropagation(...)

    References
    ----------
    Xiaojin Zhu and Zoubin Ghahramani. Learning from labeled and unlabeled data
    with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon
    University, 2002 http://pages.cs.wisc.edu/~jerryzhu/pub/CMU-CALD-02-107.pdf

    See Also
    --------
    LabelSpreading : Alternate label propagation strategy more robust to noise
    """

    def _build_graph(self):
        """Matrix representing a fully connected graph between each sample

        This basic implementation creates a non-stochastic affinity matrix, so
        class distributions will exceed 1 (normalization may be desired).
        """
        if self.kernel == 'knn':
            self.nn_fit = None
        affinity_matrix = self._get_kernel(self.X_)
        normalizer = affinity_matrix.sum(axis=0)
        if sparse.isspmatrix(affinity_matrix):
            affinity_matrix.data /= np.diag(np.array(normalizer))
        else:
            affinity_matrix /= normalizer[:, np.newaxis]
        return affinity_matrix


class LabelSpreading(BaseLabelPropagation):
    """LabelSpreading model for semi-supervised learning

    This model is similar to the basic Label Propagation algorithm,
    but uses affinity matrix based on the normalized graph Laplacian
    and soft clamping across the labels.

    Read more in the :ref:`User Guide <label_propagation>`.

    Parameters
    ----------
    kernel : {'knn', 'rbf', callable}
        String identifier for kernel function to use or the kernel function
        itself. Only 'rbf' and 'knn' strings are valid inputs. The function
        passed should take two inputs, each of shape [n_samples, n_features],
        and return a [n_samples, n_samples] shaped weight matrix

    gamma : float
      parameter for rbf kernel

    n_neighbors : integer > 0
      parameter for knn kernel

    alpha : float
      clamping factor

    max_iter : float
      maximum number of iterations allowed

    tol : float
      Convergence tolerance: threshold to consider the system at steady
      state

    n_jobs : int, optional (default = 1)
        The number of parallel jobs to run.
        If ``-1``, then the number of jobs is set to the number of CPU cores.

    Attributes
    ----------
    X_ : array, shape = [n_samples, n_features]
        Input array.

    classes_ : array, shape = [n_classes]
        The distinct labels used in classifying instances.

    label_distributions_ : array, shape = [n_samples, n_classes]
        Categorical distribution for each item.

    transduction_ : array, shape = [n_samples]
        Label assigned to each item via the transduction.

    n_iter_ : int
        Number of iterations run.

    Examples
    --------
    >>> from sklearn import datasets
    >>> from sklearn.semi_supervised import LabelSpreading
    >>> label_prop_model = LabelSpreading()
    >>> iris = datasets.load_iris()
    >>> random_unlabeled_points = np.where(np.random.randint(0, 2,
    ...    size=len(iris.target)))
    >>> labels = np.copy(iris.target)
    >>> labels[random_unlabeled_points] = -1
    >>> label_prop_model.fit(iris.data, labels)
    ... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
    LabelSpreading(...)

    References
    ----------
    Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston,
    Bernhard Schoelkopf. Learning with local and global consistency (2004)
    http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.115.3219

    See Also
    --------
    LabelPropagation : Unregularized graph based semi-supervised learning
    """

    def __init__(self, kernel='rbf', gamma=20, n_neighbors=7, alpha=0.2,
                 max_iter=30, tol=1e-3, n_jobs=1):

        # this one has different base parameters
        super(LabelSpreading, self).__init__(kernel=kernel, gamma=gamma,
                                             n_neighbors=n_neighbors,
                                             alpha=alpha, max_iter=max_iter,
                                             tol=tol,
                                             n_jobs=n_jobs)

    def _build_graph(self):
        """Graph matrix for Label Spreading computes the graph laplacian"""
        # compute affinity matrix (or gram matrix)
        if self.kernel == 'knn':
            self.nn_fit = None
        n_samples = self.X_.shape[0]
        affinity_matrix = self._get_kernel(self.X_)
        laplacian = graph_laplacian(affinity_matrix, normed=True)
        laplacian = -laplacian
        if sparse.isspmatrix(laplacian):
            diag_mask = (laplacian.row == laplacian.col)
            laplacian.data[diag_mask] = 0.0
        else:
            laplacian.flat[::n_samples + 1] = 0.0  # set diag to 0.0
        return laplacian
